The present invention, in some embodiments thereof, relates to a method and system for processing medical images to produce images with reduced noise and other desirable characteristics, more particularly, but not exclusively, to a method of processing CT images that takes into account non-uniform distribution of noise in the image, and/or uses nonlinear filters to retain fine detail.
E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt and J. M. Ogden, “Pyramid methods in image processing”, RCA Engineer, 29-6, November 1984, describes a method of fusing two images of a scene taken at different camera focus settings, using a Laplacian pyramid decomposition of the images.
Hui Li, B. S. Manjunath, and S. K. Mitra, “Multi-sensor image fusion using the wavelet transform,” in Proceedings of IEEE International Conference on Image Processing, 1994, describes fusing different images of a same region, obtained using different types of sensors, using a wavelet transform instead of a Laplacian pyramid.
Yu Lifeng, Zu Donglin, Wan Weidong, and Bao Shanglian, “Multi-Modality Medical Image Fusion Based on Wavelet Pyramid and Evaluation,” a report published by the Institute of Heavy Ion Physics, Peking University, Beijing 100871, China (2001), describes a method of fusing two medical images obtained using different imaging modalities, for example CT and MRI, or PET and MRI, using a wavelet pyramid.
Hassam El-Din Moustafa and Sameh Rehan, “Applying Image Fusion Techniques for Detection of Hepatic Lesions,” Proceedings of the 6th WSEAS International Conference on Wavelet Analysis & Multirate Systems, Bucharest, Romania, Oct. 16-18, 2006, pages 41-44, compares the results of fusing a CT and an MRI image, using different methods, including the Laplacian pyramid, the wavelet transform, the Computationally Efficient Pixel-level Image Fusion method, and the Multi-focus Technique based on Spatial Frequency.
Richard Alan Peters II, “A New Algorithm for Image Noise Reduction using Mathematical Morphology”, IEEE Trans. Image Processing 4, 554-568 (1995), describes a morphological image cleaning algorithm that preserves thin features while removing noise. The method calculates residual images on a number of different scales via a morphological size distribution, and discards regions in the various residuals that it judges to contain noise, provided the noise has a smaller dynamic range than the thin features.
US2008/0310695 to Garnier et al, describes a method of denoising an MRI image using a locally adaptive nonlinear noise filter, taking into account the spatial variation of noise level theoretically expected in the MRI image.
US2008/0118128 to Toth, describes generating a simulated image that has a predetermined amount of artificially generated noise added to it.
The following publications and patents relate generally to image processing noise reduction, image acquisition and/or computer vision:    US 2007/053477—Method and apparatus of global de-noising for cone beam and fan beam CT imaging;    KR 2005-0031210—Method and apparatus of image denoising;    JP 2000-050109—Nonlinear image filter for removing noise;    U.S. Pat. No. 6,459,755—Method and apparatus for administrating low dose CT scans;    US 2003/099405—CT dose reduction filter with a computationally efficient implementation;    EP 1 774 837—Active dose reduction device and method;    JP 2001-39874—Magnetic field generator for MRI;    WO 2007/047599—Method and apparatus for high gain magnetic resonance;    Steven Haker, Lei Zhu, Allen Tannenbaum, and Sigurd Angenent, “Optimal Mass Transport for Registration and Warping”, International Journal of Computer Vision, Volume 60, Issue 3 (December 2004), Pages 225-240;    Yossi Rubner, Carlo Tomasi, and J. Leonidas Guibas, “A Metric for Distributions with Applications to Image Databases”, ICIP 1998, Pages 59-66;    Belongie Serge, Jitendra Malik, and Puzicha Jan, “Shape Matching and Object Recognition Using Shape Contexts”, IEEE T-PAMI, Volume 24, No. 4, (April 2002);    Robert Osada, Thomas Funkhouser, Bernard Chazelle, and David Dobkin, “Matching 3D Models with Shape Distributions”, Proceedings of the International Conference on Shape Modeling & Applications 2001, Pages 154-166;    P. J. Burt and E. H. Adelson, “The Laplacian Pyramid as a Compact Image Code”, IEEE Trans. on Communications, pp. 532-540, April 1983;    Iddo Drori, Daniel Cohen-Or, and Hezy Yeshurun, “Fragment based image completion”, ACM Transactions on Graphics 22(3), (Proc. of SIGGRAPH 2003), 303-312;    John Goutsias and Henk J. A. M. Heijmans, “Nonlinear Multiresolution Signal Decomposition Schemes—Part I: Morphological Pyramids”, IEEE Trans. on Image Processing, Vol. 9, No. 11, November 2000;    John Goutsias and Henk J. A. M. Heijmans, “Nonlinear Multiresolution Signal Decomposition Schemes—Part II: Morphological Wavelets”, IEEE Trans. on Image Processing, Vol. 9, No. 11, November 2000;    Jean Serra, “Image Analysis and Mathematical Morphology”, 1982;    A. J. Britten, M. Crotty, H. Kiremidjian, A. Grundy, and E. J. Adam, “The addition of computer simulated noise to investigate radiation dose and image quality in images with spatial correlation of statistical noise: an example application to X-ray CT of the brain”, The British Journal of Radiology, 77 (2004), 323-328;    C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images”, in Proceedings of the 6th International Conference in Computer Vision (ICCV), 1998, pp. 839-846;    J. Weickert, “Coherence-Enhancing Diffusion Filtering”, International Journal of Computer Vision, 31(2-3), pp. 111-127, 1999;    A. Buades, B. Coll, and J.-M. Morel, “On image denoising methods”, Centre de Mathématiques et de Leurs Applications (CMLA) publication No. 2004-15, 2004;    P. Coupé et al, “Fast Non Local Means Denoising for 3D MR Images,” 9th International Conference on MICCAI 2006, R. Larsen, M. Nielsen, J. Sporring (eds.), Lecture Notes in Computer Science, Vol. 4191, pp. 33-40, Copenhagen, Denmark, October 2006;    M. Mahmoudi and G. Sapiro, “Fast Image and Video Denoising via Nonlocal Means Of Similar Neighborhoods,” IEEE Signal Processing letters 12, 839-842 (2005);    A. Heiderzadeh and A. N. Avanaki, “An Enhanced Nonlocal-Means Algorithm for Image Denoising,” Proc. IEEE 9th International Symposium on Signal Processing and its Applications (ISSPA'07), pp. 1-4, Sharjah, UAE, 2007;    N. Azzabou et al, “Image Denoising Based on Adaptive Dictionary Computation,” Proceedings of IEEE International Conference on Image Processing, 2007.